Nouman Azam

Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets

Probabilistic rough set approach defines the positive, negative and boundary regions, each associated with a certain level of uncertainty. A pair of threshold values determines the uncertainty levels in these regions. A critical issue in the community is the determination of optimal values of these thresholds. This problem may be investigated by considering a possible relationship between changes in probabilistic thresholds and their impacts on uncertainty levels of different regions. We investigate the use of game-theoretic rough set (GTRS) model in exploring such a relationship. A threshold configuration mechanism is defined with the GTRS model in order to minimize the overall uncertainty level of rough set based classification. By realizing probabilistic regions as players in a game, a mechanism is introduced that repeatedly tunes the parameters in order to calculate effective threshold parameter values. Experimental results on text categorization suggest that the overall uncertainty of probabilistic regions may be reduced with the threshold configuration mechanism.

Comparison of term frequency and document frequency based feature selection metrics in text categorization

Text categorization plays an important role in applications where information is filtered, monitored, personalized, categorized, organized or searched. Feature selection remains as an effective and efficient technique in text categorization. Feature selection metrics are commonly based on term frequency or document frequency of a word. We focus on relative importance of these frequencies for feature selection metrics. The document frequency based metrics of discriminative power measure and GINI index were examined with term frequency for this purpose. The metrics were compared and analyzed on Reuters 21,578 dataset. Experimental results revealed that the term frequency based metrics may be useful especially for smaller feature sets. Two characteristics of term frequency based metrics were observed by analyzing the scatter of features among classes and the rate at which information in data was covered. These characteristics may contribute toward their superior performance for smaller feature sets.

Web-Based Medical Decision Support Systems for Three-Way Medical Decision Making With Game-Theoretic Rough Sets

The realization of the Web as a common platform, medium, and interface for supporting human activities has attracted many researchers to the study of Web-based support systems (WSS). An important branch of WSS is Web-based decision support systems that provide intelligent support for making effective decisions in different domains. We focus on decision making in Web-based medical decision support systems (WMDSS). Uncertainty is a critical factor that affects decision making and reasoning in the medical field. A three-way decision-making approach is an effective and better choice to lessen the effects of uncertainty. It provides the provision for delaying certain or definite decisions in situations that lack sufficient evidence or accurate information in reaching certain conclusions. Particularly, the option of deferment decisions is added in this approach that provides the flexibility to further examine and investigate the uncertain and doubtful cases. The game-theoretic rough set (GTRS) model is a recent development in rough sets that can be used to determine the three rough set regions in the probabilistic rough sets framework by determining a pair of thresholds. The three regions are used to obtain three-way decision rules in the form of acceptance, rejection, and deferment rules. In this paper, we extend the GTRS model to analyze uncertainty involved in medical decision making. Experimental results with a GTRS-based approach on different health care datasets suggest that the approach may improve the overall quality of decision making in the medical field, as well as other fields. It is hoped that the incorporation of a GTRS component in WMDSS will enrich and enhance its decision-making capabilities.

Multiple criteria decision analysis with game-theoretic rough sets

Multiple criteria decision analysis plays an important role in many real life problems found in business, economics, management, governmental and political disputes. The game-theoretic rough set model (GTRS) is a recent extension to rough set theory for intelligent decision making observed with game-theoretic formulation. In this article, we extend GTRS for formulating and analyzing multiple criteria decision making problems in rough sets. Basic concepts of the model are defined, reviewed and analyzed in the context of multiple criteria. Applicability of GTRS is demonstrated by considering different examples, including multiple criteria effective classification, rule mining and feature selection.

A three-way decision making approach to malware analysis using probabilistic rough sets

We employ three-way decisions approach to malware analysis using probabilistic rough sets.Architecture for malware analysis based on three-way decisions is proposed.Experimental results on UNM dataset advocates for the use of three-way decisions in malware analysis. Malware analysis aims to identify malware by examining applications behaviour on the host operating system. A common issue in malware analysis is how to mitigate and handle the false decisions such as false positives. Existing approaches which are based on two-way decisions (such as acceptance and rejection) for classifying applications behaviour result in two shortcomings. Firstly, the two-way decisions are rigid and strict in the sense that they demand that a classification decision must be made irrespective of the quality of available information. This potentially leads to wrong classification decisions whenever we do not have sufficient and complete information. Secondly, two-way decisions do not involve any explicit mechanism for dealing with the false decisions at the model level. The existing approaches generally work like an add-on to learning models and are only exercised after incorrect decisions are being made by the learning models. This results in additional processing and increases the complexity of the task. In this paper, we investigate a three-way decision making approach based on decisions of acceptance, rejection or deferment. The added deferment decision option provides flexibility for delaying a certain decision whenever we do not have sufficient information. Moreover, it aims to mitigate the false decisions at the model level by determining a tradeoff between different properties of decision making such as accuracy, generality and uncertainty. We considered three-way decisions based on two probabilistic rough set models, namely, game-theoretic rough setsź(GTRS) and information-theoretic rough setsź(ITRS) in this study. An architecture of malware analysis realized with probabilistic rough sets based three-way decisions is proposed. A new algorithm termed as sequentially stackable linux securityź(SSLS) based on the proposed architecture is presented. Experimental results on the system call sequences from the UNM data set advocate for the use of three-way decisions in malware analysis.

Interpretation of equilibria in game-theoretic rough sets

The GTRS provides a mechanism for determining a pair of thresholds in the probabilistic rough set model.The equilibria play a critical role in determining thresholds in the GTRS model.We address two equilibria related issues with the GTRS model.The first issue is the interpretation of equilibria.The second issue in the establishment of equilibria. Intelligent decision making models aim at improving the quality of decision making under uncertainty. The fundamental issues that are generally encountered in these models are too many options to choose from and the involvement of contradictory decision making criteria. The game-theoretic rough set (GTRS) model provides an intelligent decision making mechanism that exploits a game-theoretic environment for analyzing strategic situations between cooperative or conflicting decision making criteria in the probabilistic rough set framework. The concept of equilibria is of central importance in the GTRS model which has not been sufficiently addressed in the current literature. Two key issues in this regard are the interpretation of equilibria and the establishment of their existence. By reviewing, examining and defining the basic game constructs in the GTRS model, we are able to interpret an equilibrium in terms of the decision thresholds that control the rough sets based decision regions. In particular, an equilibrium is defined in terms of a pair of thresholds such that no player has a unilateral incentive to change these thresholds within the game. An example game is considered to demonstrate the use of the interpretation in determining the thresholds. The issue of existence of equilibria is addressed by considering a couple of typical two-player games in the GTRS model. The results suggest that the existence of equilibria may be established under certain limited conditions.

Classifying Attributes with Game-Theoretic Rough Sets

Attributes may be categorized as core, reduct or non-reduct attributes when rough set theory is utilized for classification. These attribute types play different roles in feature selection algorithms. We introduce a game-theoretic rough set based method that formulates the classification of an attribute as a decision problem within a game. In particular, multiple measures representing importance levels for an attribute are incorporated into a unified framework to obtain an effective attribute classification mechanism. Demonstrative example suggests that the method may be efficient in classifying different types of attributes.

Formulating Game Strategies in Game-Theoretic Rough Sets

The determination of thresholds α,β has been considered as a fundamental issue in probabilistic rough sets. The game-theoretic rough seti¾?GTRS model determines the required thresholds based on a formulated game between different properties related to rough sets approximations and classification. The game strategies in the GTRS model are generally based on an initial threshold configuration that corresponds to the Pawlak model. We study different approaches for formulating strategies by considering different initial conditions. An example game is shown for each case. The selection of a particular approach for a given problem may be based on the quality of data and computing resources at hand. The realization of these approaches in GTRS based methods may bring new insights into effective determination of probabilistic thresholds.

Incorporating game theory in feature selection for text categorization

Feature selection remains as one of effective and efficient techniques in text categorization. Selecting important features is crucial for effective performance in case of high imbalance in data.We introduced a method which incorporates game theory to feature selection with the aim of dealing with high imbalance situations for text categorization. In particular, a game is formed between negative and positive categories to identify the suitability of features for their respective categories. Demonstrative example suggests that this method may be useful for feature selection in text categorization problems involving high imbalance.

Evaluation functions and decision conditions of three-way decisions with game-theoretic rough sets

Three-way decisions have been used over the years in many application areas. The rough sets and its extensions provide useful approaches for three-way decisions. Typically, these approaches employ a single evaluation function or criterion to induce three-way decisions. When extending the rough set based three-way decisions to multiple criteria decision making (MCDM), two issues are encountered. The first issue is related to the construction and definition of aggregation mechanisms for dealing with differences in results of evaluation functions. The second issue is related to the setting of choice structure for selecting the three types of decision choices. In this article, we consider the role and use of game-theoretic rough set (GTRS) model to resolve and address these two issues. The issue related to differences in evaluation functions is addressed with GTRS by implementing a game that considers multiple evaluation functions as game players. The game-theoretic analysis in the GTRS is employed to resolve the differences by determining a tradeoff between evaluation functions. The issue related to choice structure is addressed by considering the conditions under which different game outcomes could constitute a game solution. In particular, the equilibrium analysis within games is used to construct the rules for three-way decisions. A demonstrative example is used to explain the use of the proposed approach. The relationship between the proposed approach and the probabilistic rough sets is also discussed.